{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# 1. 了解 Matplotlib\n",
        "\n",
        "## 知识点讲解\n",
        "\n",
        "### Matplotlib 是什么\n",
        "\n",
        "Matplotlib 是 Python 中最流行的数据可视化库之一，用于创建各种静态、动态和交互式图表。\n",
        "\n",
        "### 用途和优势\n",
        "\n",
        "- **用途**：\n",
        "  - 数据可视化：将数据转换为直观的图表\n",
        "  - 科学计算可视化：绘制函数图像、数据分布等\n",
        "  - 报告和演示：生成高质量的图表用于报告\n",
        "  - 数据分析：探索性数据分析（EDA）\n",
        "\n",
        "- **优势**：\n",
        "  - 功能强大：支持多种图表类型（线图、散点图、柱状图、直方图等）\n",
        "  - 高度可定制：可以精确控制图表的每个细节\n",
        "  - 跨平台：支持 Windows、macOS、Linux\n",
        "  - 与 NumPy、Pandas 等库完美集成\n",
        "  - 支持多种输出格式（PNG、PDF、SVG 等）\n",
        "\n",
        "### Matplotlib 的架构\n",
        "\n",
        "Matplotlib 采用三层架构设计：\n",
        "\n",
        "1. **Backend 层（后端层）**\n",
        "   - 负责实际的渲染工作\n",
        "   - 处理不同平台的图形输出\n",
        "   - 例如：TkAgg、Qt5Agg、Agg 等\n",
        "\n",
        "2. **Artist 层（艺术家层）**\n",
        "   - 提供面向对象的绘图接口\n",
        "   - 包含各种图形元素（Figure、Axes、Line2D 等）\n",
        "   - 提供精确的图形控制\n",
        "\n",
        "3. **Scripting 层（脚本层）**\n",
        "   - Pyplot 接口，提供函数式绘图\n",
        "   - 适合快速绘图和交互式使用\n",
        "   - 自动管理图形和坐标轴\n",
        "\n",
        "### 核心概念\n",
        "\n",
        "- **Figure（图形）**：整个绘图窗口，可以包含多个子图\n",
        "- **Axes（坐标轴）**：实际的绘图区域，包含坐标轴、刻度、标签等\n",
        "- **Canvas（画布）**：底层的绘图区域，由 Backend 管理\n",
        "\n",
        "### 安装和导入\n",
        "\n",
        "**安装方法**：\n",
        "```bash\n",
        "pip install matplotlib\n",
        "# 或\n",
        "conda install matplotlib\n",
        "```\n",
        "\n",
        "**导入方式**：\n",
        "```python\n",
        "import matplotlib.pyplot as plt\n",
        "```\n",
        "\n",
        "这是最常用的导入方式，`plt` 是约定俗成的别名。\n",
        "\n",
        "### 版本检查\n",
        "\n",
        "**语法格式**：\n",
        "```python\n",
        "matplotlib.__version__\n",
        "```\n",
        "\n",
        "**返回值说明**：\n",
        "- 返回字符串类型，表示当前安装的 Matplotlib 版本号\n",
        "- 例如：`'3.7.1'`\n",
        "\n",
        "**适用场景**：\n",
        "- 检查安装是否成功\n",
        "- 确认版本是否符合要求\n",
        "- 调试版本相关的问题\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Matplotlib 版本: 3.10.7\n",
            "Matplotlib 导入成功！\n"
          ]
        }
      ],
      "source": [
        "# 基础示例：检查 Matplotlib 是否安装成功\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# 检查版本\n",
        "print(f\"Matplotlib 版本: {matplotlib.__version__}\")\n",
        "\n",
        "# 验证导入是否成功\n",
        "print(\"Matplotlib 导入成功！\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Figure 对象: Figure(640x480)\n",
            "Axes 对象: Axes(0.125,0.11;0.775x0.77)\n",
            "Figure 大小: [6.4 4.8]\n",
            "Axes 数量: 1\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 进阶示例：了解 Matplotlib 的架构组件\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# 创建一个简单的图形\n",
        "fig, ax = plt.subplots()\n",
        "\n",
        "# 绘制数据\n",
        "# 在区间 [0, 10] 上生成 100 个等间隔的点，用于绘图的横坐标\n",
        "x = np.linspace(0, 10, 100)\n",
        "y = np.sin(x)\n",
        "ax.plot(x, y)\n",
        "\n",
        "# 查看 Figure 和 Axes 对象\n",
        "print(f\"Figure 对象: {fig}\")\n",
        "print(f\"Axes 对象: {ax}\")\n",
        "print(f\"Figure 大小: {fig.get_size_inches()}\")\n",
        "print(f\"Axes 数量: {len(fig.axes)}\")\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# 实际应用场景：快速数据可视化\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from matplotlib import font_manager\n",
        "\n",
        "# 设置支持中文字体（如微软雅黑，如果未安装会降级）\n",
        "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Arial Unicode MS']  # 依赖于系统已安装字体\n",
        "plt.rcParams['axes.unicode_minus'] = False  # 正常显示负号\n",
        "\n",
        "\n",
        "# 模拟销售数据\n",
        "months = ['1月', '2月', '3月', '4月', '5月', '6月']\n",
        "sales = [120, 135, 150, 145, 160, 175]\n",
        "\n",
        "# 创建图表\n",
        "# 设置画布大小为 8x5 英寸\n",
        "plt.figure(figsize=(8, 5))\n",
        "# 绘制折线图，带有圆形标记、线宽 2、标记大小 8\n",
        "plt.plot(months, sales, marker='o', linewidth=2, markersize=8)\n",
        "# 添加主标题，字号 14，加粗\n",
        "plt.title('月度销售额趋势', fontsize=14, fontweight='bold')\n",
        "# 设置横轴标签及字号\n",
        "plt.xlabel('月份', fontsize=12)\n",
        "# 设置纵轴标签及字号\n",
        "plt.ylabel('销售额（万元）', fontsize=12)\n",
        "# 添加网格线，透明度为 0.3\n",
        "plt.grid(True, alpha=0.3)\n",
        "# 自动调整子图参数以适应画布\n",
        "# plt.tight_layout() 的作用是自动调整子图参数，使其填充整个图像区域，避免标签、标题等文本重叠，从而使图表布局更加美观。\n",
        "plt.tight_layout()\n",
        "# 显示图表\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 常见错误和注意事项\n",
        "\n",
        "1. **导入错误**\n",
        "   - 错误：`ModuleNotFoundError: No module named 'matplotlib'`\n",
        "   - 解决：使用 `pip install matplotlib` 安装\n",
        "\n",
        "2. **版本兼容性**\n",
        "   - 不同版本的 Matplotlib API 可能有差异\n",
        "   - 建议使用较新的稳定版本（3.0+）\n",
        "\n",
        "3. **导入顺序**\n",
        "   - 建议先导入 NumPy，再导入 Matplotlib\n",
        "   - 某些情况下需要设置 Backend 后再导入\n",
        "\n",
        "4. **Jupyter Notebook 中的显示**\n",
        "   - 需要使用 `%matplotlib inline` 才能在 Notebook 中显示图表\n",
        "   - 或者使用 `plt.show()` 显示图表\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## 总结\n",
        "\n",
        "### 最佳实践\n",
        "\n",
        "1. **导入规范**：始终使用 `import matplotlib.pyplot as plt`\n",
        "2. **版本管理**：在项目开始时检查并记录 Matplotlib 版本\n",
        "3. **架构理解**：理解三层架构有助于选择合适的绘图方式\n",
        "4. **文档查阅**：遇到问题时查阅官方文档\n",
        "\n",
        "### 性能建议\n",
        "\n",
        "- 对于大量数据点，考虑使用 `numpy` 数组而非 Python 列表\n",
        "- 在循环中绘图时，使用面向对象接口更高效\n",
        "- 批量绘图时，考虑使用 `plt.subplots()` 一次性创建多个子图\n",
        "\n",
        "### 下一步学习\n",
        "\n",
        "- 学习两种绘图接口的使用\n",
        "- 掌握基本绘图流程\n",
        "- 了解如何配置中文字体\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "ml311",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}
